






'''simple docstring'''







import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()


def       __UpperCamelCase					(      lowercase_       :  Any   ,       lowercase_       :  Optional[int]   ):
       """simple docstring"""




       a_						     =			torch.load(lowercase_   ,       map_location='cpu'   )

       a_						     =			chkpt['model']

       # We have the base model one level deeper than the original XLM repository
       a_						     =			{}
       for k, v in state_dict.items():
              if "pred_layer" in k:
                     a_						     =			v
              else:
                     a_						     =			v

       a_						     =			chkpt['params']
       a_						     =			{n: v for n, v in config.items() if not isinstance(lowercase_   ,       (torch.FloatTensor, numpy.ndarray)   )}

       a_						     =			chkpt['dico_word2id']
       a_						     =			{s + '</w>' if s.find('@@'   ) == -1 and i > 13 else s.replace('@@'   ,       ''   ): i for s, i in vocab.items()}

       # Save pytorch-model
       a_						     =			pytorch_dump_folder_path + '/' + WEIGHTS_NAME
       a_						     =			pytorch_dump_folder_path + '/' + CONFIG_NAME
       a_						     =			pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['vocab_file']

       print(F'Save PyTorch model to {pytorch_weights_dump_path}'   )
       torch.save(lowercase_   ,       lowercase_   )

       print(F'Save configuration file to {pytorch_config_dump_path}'   )
       with open(lowercase_   ,       'w'   ,       encoding='utf-8'   ) as f:
              f.write(json.dumps(lowercase_   ,       indent=2   ) + '\n'   )

       print(F'Save vocab file to {pytorch_config_dump_path}'   )
       with open(lowercase_   ,       'w'   ,       encoding='utf-8'   ) as f:
              f.write(json.dumps(lowercase_   ,       indent=2   ) + '\n'   )


if __name__ == "__main__":
  __lowerCAmelCase        =							argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--xlm_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
  )
  __lowerCAmelCase        =							parser.parse_args()
  convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)












'''simple docstring'''







def       __UpperCamelCase					(      lowercase_       :  int = 1_000_000   ):
       """simple docstring"""




       a_						     =			set(range(3   ,       lowercase_   ,       2   )   )
       primes.add(2   )
       for p in range(3   ,       lowercase_   ,       2   ):
              if p not in primes:
                     continue
              primes.difference_update(set(range(p * p   ,       lowercase_   ,       lowercase_   )   )   )

       a_						     =			[float(lowercase_   ) for n in range(limit + 1   )]

       for p in primes:
              for n in range(lowercase_   ,       limit + 1   ,       lowercase_   ):
                     phi[n] *= 1 - 1 / p

       return int(sum(phi[2:]   )   )


if __name__ == "__main__":
  print(f"""{solution() = }""")




